1. Technical Field
The present disclosure relates generally to the field of automatic feedback systems for devices, machines, and systems in the presence of uncertainty and/or risk.
2. Description of the Related Technology
In classical control systems, a linear dynamic model of a control object is obtained in the form of dynamic equations, usually ordinary differential or difference equations. Based on the linear dynamic model, a controller can be designed by assuming the linear dynamic model approximates the control object. Under this approach, the control object is assumed to be relatively linear and time invariant. However, many real control objects can be time varying, strongly nonlinear, and unstable. For example, the dynamic model may include parameters (e.g., masses, inductance, aerodynamics coefficients, etc.) that depend on a changing environment or a state of the dynamic model. If the parameter variation is small and the dynamic model is stable, then a proportional-integral-derivative (PID) controller can be satisfactory.
Modern multivariable control methodologies, such as H∞ or mixed-μ controllers synthesis, can account for some degree of uncertainty in the control object's parameters and/or dynamics. Like classic control systems, controller design is based on the assumption that the control object can be sufficiently approximated by a linear dynamic model. For the purpose of controller design, time variations and nonlinearities can be treated as “uncertainty” in the problem formulation. As a result, strongly time-varying and/or nonlinear control objects can result in a linear dynamic model with an unnecessarily large characterization of its uncertainty. However, for sufficiently large uncertainty, there may not be a controller that can stabilize the control object, particularly for all possible forms of uncertainty. Further, the resulting controller, if it exists, can be conservative in the sense that it accounts for the worst case realization of the uncertainty.
There are several nonlinear control techniques, such as feedback linearization, dynamic inversion, sliding mode, nonlinear damping, adaptation (for example, online parameter estimation), or the like. However, there does not appear to be a general nonlinear control methodology that takes into account formal uncertainty and performance requirements.
In quantum computing, classical computers with a Von Neumann architecture can simulate quantum algorithm gates for performing quantum algorithms and computation. A quantum algorithm can be solved using an algorithmic-based approach, wherein matrix elements of the quantum gate are calculated on demand. For example, an implementation of a Grover's search algorithm can be executed on a classic computer.